To model ITS data effectively, we must acknowledge and leverage asynchronous observations [1]}.
Specifically, successful ITS classifiers should consider patterns of irregularity; different classes often have unique signatures in their observations' timing [2]}.
For example, diabetes-risk patients receive more blood sugar tests while in the hospital.
The early classification community has yet to consider this common, rich, and general type of time series data.
